Author

Ou Li

Date of Award

12-7-2006

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Xiaolin Hu - Chair

Second Advisor

Dr. Raj Sunderraman

Third Advisor

Dr. Ying Zhu

Abstract

One mechanism for an intelligent agent to adapt to substantial environmental changes is to change its decision making structure. Pervious work in this area has developed a context-dependent behavior selection architecture that uses structure change, i.e., changing the mutual inhibition structures of a behavior network, as the main mechanism to generate different behavior patterns according to different behavioral contexts. Given the important of network structure, this work investigates how the structure of a behavior network can be learned. We developed a structure learning method based on generic algorithm and applied it to a model crayfish that needs to survive in a simulated environment. The model crayfish is controlled by a mutual inhibition behavior network, whose structures are learned using the GA-based algorithm for different environment configurations. The results show that it is possible to learn robust and consistent network structures allowing intelligent agents to behave adaptively in a particular environment.

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